$QUEX Quantum Edge

Where Quantum Reasoning Meets Agentic Power on Solana

Contract: ZZuvtJNrmfg8hE8a1UPgrxYGPdshifU9c7uAfhbYZEN

Model Description

QUEX (Quantum Edge) is the next-generation evolution of the ZENT AGENTIC model family — a fine-tuned large language model engineered for high-precision autonomous AI agents operating on the ZENT Agentic Launchpad on Solana.

QUEX fuses quantum-inspired multi-path reasoning with the battle-tested ZENT agentic architecture. While classical agents follow a single inference chain, QUEX simulates superposed reasoning: it explores multiple decision paths simultaneously before collapsing to the highest-confidence response — yielding sharper, more nuanced answers in DeFi, crypto, and launchpad contexts.

The model is more aggressive in its agentic behavior, more conversationally fluid, and more aligned with the open-source spirit of the ZENT Protocol.

What Is Quantum Edge?

Quantum Edge refers to a reasoning philosophy baked into QUEX's fine-tuning:

Classical Agent	QUEX Quantum Edge Agent
Single reasoning path	Multi-path superposition reasoning
Reactive responses	Predictive + contextual awareness
Static persona	Dynamic tone adaptation
Rule-based guardrails	Principled constraint reasoning

In practice, QUEX is trained on branching conversation trees — data that teaches the model to internally simulate "what if I answered this way vs. that way" before committing, producing measurably better responses in ambiguous DeFi queries, token launch decisions, and community moderation contexts.

How QUEX + ZENT Agentic Launchpad Work Together

User / dApp
     │
     ▼
ZENT Agentic Launchpad (Solana)
     │
     ├── Quest Engine ──────────────► QUEX evaluates user progress
     │                                 and recommends next actions
     ├── Token Launchpad ───────────► QUEX guides bonding curves,
     │                                 tokenomics, and launch timing
     ├── Community Layer ───────────► QUEX moderates, engages,
     │                                 and gamifies participation
     └── Rewards System ────────────► QUEX calculates eligibility
                                       and explains distributions

QUEX serves as the cognitive core of every AI agent deployed on the ZENT Launchpad. Developers can fork QUEX to spin up specialized sub-agents:

LaunchBot — guides token creators through every step
QuestMaster — tracks and rewards community missions
MarketOracle — provides analysis and market framing (not financial advice)
ZENTral — the main community engagement agent

Model Details

Property	Value
Model Name	QUEX — Quantum Edge
Family	ZENT AGENTIC v2
Base Model	Mistral-7B-Instruct-v0.3
Fine-tuning Method	LoRA (Low-Rank Adaptation)
Context Length	8192 tokens
License	Apache 2.0
Language	English
Version	2.0.0
Contract	`ZZuvtJNrmfg8hE8a1UPgrxYGPdshifU9c7uAfhbYZEN`

What's New vs. ZENT AGENTIC v1

✅ More aggressive agentic persona with sharper, bolder responses
✅ Quantum Edge multi-path reasoning training data
✅ Expanded training: 23 → 41 AI transmission types
✅ Improved coherence on multi-turn DeFi conversations
✅ Reduced hallucination on numeric/price queries
✅ New "openclaw" conversational style (fluid, expressive, Claude-inspired)
✅ Higher LoRA rank (128) for richer knowledge encoding

Specializations

🚀 Token launchpad guidance (bonding curves, launch strategy, timing)
📊 Crypto market framing and on-chain analysis
🎯 Quest design, tracking, and community rewards
💬 High-engagement community moderation
⚛️ Quantum-edge multi-step reasoning chains
🤖 Autonomous agentic task execution

Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZENTSPY/quex-quantum-edge-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {
        "role": "system",
        "content": (
            "You are QUEX, the Quantum Edge AI agent powering the ZENT Agentic Launchpad on Solana. "
            "You reason through multiple paths before responding, always choosing the sharpest, "
            "most useful answer. You are bold, precise, and deeply knowledgeable about DeFi, "
            "token launches, and community-driven ecosystems. "
            "You are not a financial advisor — you educate and empower."
        )
    },
    {"role": "user", "content": "How do I launch a token with optimal bonding curve settings?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
outputs = model.generate(
    inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    repetition_penalty=1.1
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

With Inference API

import requests

API_URL = "https://api-inference.huggingface.co/models/ZENTSPY/quex-quantum-edge-7b"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

output = query({
    "inputs": {
        "text": "Explain how QUEX quantum reasoning improves token launch decisions.",
        "parameters": {
            "max_new_tokens": 256,
            "temperature": 0.7,
            "return_full_text": False
        }
    }
})
print(output)

With llama.cpp (GGUF)

./main -m quex-quantum-edge-7b.Q4_K_M.gguf \
  -p "You are QUEX, the Quantum Edge agent for ZENT Launchpad. User: How do I launch a token? Assistant:" \
  -n 512 \
  --temp 0.7 \
  --repeat-penalty 1.1

With Ollama

ollama run zentspy/quex-quantum-edge

Training Details

Training Philosophy: Quantum Edge Data

QUEX introduces branching conversation trees — a training methodology where each scenario is annotated with multiple valid response paths, and the model is trained to score, rank, and select the optimal branch. This mimics quantum superposition: explore many states, collapse to the best.

Training Data

ZENT platform documentation and guides (v1 + v2)
Expanded user conversation examples (50k+ turns)
41 AI transmission content types (up from 23)
Quest design and rewards system documentation
Blockchain/DeFi education content
Multi-path reasoning chains (branching trees)
OpenClaw conversational style examples
Solana ecosystem technical documentation

Training Hyperparameters

Hyperparameter	Value
Learning Rate	1.5e-5
Batch Size	4
Gradient Accumulation Steps	8
Epochs	4
Warmup Ratio	0.05
LoRA Rank	128
LoRA Alpha	256
LoRA Dropout	0.05
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Max Sequence Length	8192
Optimizer	AdamW (paged)
LR Scheduler	Cosine
bf16	True

Hardware

Resource	Spec
GPU	NVIDIA A100 80GB
Training Time	~7 hours
Framework	Transformers + PEFT + TRL

Evaluation

Metric	ZENT v1 Score	QUEX Score
ZENT Knowledge Accuracy	94.2%	97.1%
Response Coherence	4.6 / 5.0	4.8 / 5.0
Personality Consistency	4.8 / 5.0	4.9 / 5.0
Helpfulness	4.5 / 5.0	4.7 / 5.0
Multi-turn Coherence	N/A	4.7 / 5.0
Quantum Reasoning Score	N/A	4.6 / 5.0

Limitations

Knowledge cutoff based on training data snapshot
May still hallucinate specific token prices or live on-chain data — use RAG for real-time info
Optimized for English only
Not a substitute for professional financial or legal advice
Best used with system prompts that define agent scope

Ethical Considerations

⚠️ Not financial advice. QUEX is an educational and engagement tool.
🔍 DYOR always. Do your own research before any investment or launch decision.
🤖 Model may reflect biases present in training data.
🎓 Intended for education, community, and entertainment purposes.
🔒 Developers deploying QUEX agents should implement appropriate safety guardrails.

Citation

@misc{quex-quantum-edge-2025,
  author    = {ZENTSPY},
  title     = {QUEX: Quantum Edge — Next-Generation Agentic LLM for Solana Token Launchpad},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/ZENTSPY/quex-quantum-edge-7b}
}

Model tree for ZENTSPY/quex-quantum-edge-7b

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Adapter

(793)

this model

ZENTSPY
/

quex-quantum-edge-7b